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深度神经网络集成模型：用于声学信号不确定性感知分割的深度学习方法

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.

机构信息

College of Pharmacy, University of Arizona, Tucson, AZ, United States of America.

Department of Computer Science, University of Montana, Missoula, MT, United States of America.

出版信息

PLoS One. 2023 Jul 26;18(7):e0288172. doi: 10.1371/journal.pone.0288172. eCollection 2023.

DOI:10.1371/journal.pone.0288172
PMID:37494341
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10370718/
Abstract

Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we describe our software package for labeling elements in recordings of animal sounds, and demonstrate its utility on recordings of beetle courtships and whale songs. The software, DISCO, computes sensible confidence estimates and produces labels with high precision and accuracy. In addition to the core labeling software, it provides a simple tool for labeling training data, and a visual system for analysis of resulting labels. DISCO is open-source and easy to install, it works with standard file formats, and it presents a low barrier of entry to use.

摘要

动物声音的记录使人们能够广泛地进行动物交流、行为和多样性的观察研究。在这些记录中自动标记声音事件可以提高分析的效率和可重复性。在这里，我们描述了用于标记动物声音记录中元素的软件包，并展示了其在甲虫求偶和鲸鱼歌声记录中的应用。该软件 DISCO 计算出合理的置信度估计，并生成具有高精度和高准确性的标签。除了核心的标记软件外，它还提供了一个用于标记训练数据的简单工具，以及一个用于分析生成标签的可视化系统。DISCO 是开源的，易于安装，它可以与标准文件格式一起使用，并且使用门槛很低。

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本文引用的文献

1
Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa.比较生物声学：跨不同分类群量化和比较动物声音的路线图。
Biol Rev Camb Philos Soc. 2021 Aug;96(4):1135-1159. doi: 10.1111/brv.12695. Epub 2021 Mar 2.
2
DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations.DeepSqueak：一种基于深度学习的超声发声检测与分析系统。
Neuropsychopharmacology. 2019 Apr;44(5):859-868. doi: 10.1038/s41386-018-0303-6. Epub 2019 Jan 4.
3
Purring Crickets: The Evolution of a Novel Sexual Signal.
鸣叫声如蟋蟀：一种新型性信号的进化。
Am Nat. 2018 Dec;192(6):773-782. doi: 10.1086/700116. Epub 2018 Oct 3.
4
How We Hear: The Perception and Neural Coding of Sound.我们如何听见：声音的感知与神经编码。
Annu Rev Psychol. 2018 Jan 4;69:27-50. doi: 10.1146/annurev-psych-122216-011635. Epub 2017 Oct 16.
5
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.更快的 R-CNN：基于区域建议网络的实时目标检测。
IEEE Trans Pattern Anal Mach Intell. 2017 Jun;39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031. Epub 2016 Jun 6.
6
Pitch- and spectral-based dynamic time warping methods for comparing field recordings of harmonic avian vocalizations.基于音高和频谱的动态时间规整方法在比较鸣禽谐波叫声的野外录音中的应用。
J Acoust Soc Am. 2013 Aug;134(2):1407-15. doi: 10.1121/1.4812269.
7
Silent night: adaptive disappearance of a sexual signal in a parasitized population of field crickets.寂静之夜：寄生的田间蟋蟀种群中性信号的适应性消失
Biol Lett. 2006 Dec 22;2(4):521-4. doi: 10.1098/rsbl.2006.0539.